{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T00:25:12.872586Z",
     "start_time": "2020-03-17T00:25:11.901188Z"
    }
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import linecache\n",
    "import datetime\n",
    "import sys\n",
    "import numpy as np\n",
    "import random\n",
    "from scipy.sparse import *\n",
    "from scipy import *\n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "import os.path\n",
    "import matplotlib\n",
    "import copy\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from tempfile import TemporaryFile\n",
    "import pickle\n",
    "from scipy.integrate import odeint\n",
    "from torchdiffeq import odeint as dodeint\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.nn.parameter import Parameter\n",
    "from scipy.stats import skew\n",
    "\n",
    "def flushPrint(variable):\n",
    "    sys.stdout.write('\\r')\n",
    "    sys.stdout.write('%s' % variable)\n",
    "    sys.stdout.flush() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import data\n",
    "\n",
    "## City data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T00:25:13.926658Z",
     "start_time": "2020-03-17T00:25:13.721193Z"
    }
   },
   "outputs": [],
   "source": [
    "df1=pd.read_csv('./data/county_city_province.csv')\n",
    "cities1 = set((df1['CITY']))\n",
    "df2=pd.read_csv('./data/citypopulation.csv')\n",
    "cities2 =set((df2['city']))\n",
    "cities = set(list(cities1) + list(cities2))\n",
    "nodes = {}\n",
    "city_properties = {}\n",
    "id_city = {}\n",
    "for ct in cities:\n",
    "    nodes[ct] = len(nodes)\n",
    "    city_properties[ct] = {'pop':1,'prov':'','id':-1}\n",
    "for i in df2.iterrows():\n",
    "    city_properties[i[1][0]] = {'pop':float(i[1][1])}\n",
    "for i in df1.iterrows():\n",
    "    dc = city_properties.get(i[1]['CITY'],{})\n",
    "    dc['prov'] = i[1]['PROV']\n",
    "    dc['id'] = i[1]['CITY_ID']\n",
    "    city_properties[i[1]['CITY']]=dc\n",
    "    id_city[dc['id']] = i[1]['CITY']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baidu migration flux ratio data (2016, 2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T00:25:19.660571Z",
     "start_time": "2020-03-17T00:25:15.027686Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.995342957403680654"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv('./data/city_flow_v1.csv')\n",
    "flows = {}\n",
    "for n,i in enumerate(df.iterrows()):\n",
    "    if n % 1000 == 0:\n",
    "        flushPrint(n/len(df))\n",
    "    cityi=long(i[1]['cityi.id'])\n",
    "    cityj=long(i[1]['cityj.id'])\n",
    "    value = flows.get((cityi,cityj),0)\n",
    "    flows[(cityi,cityj)] = value + i[1]['flowij']\n",
    "    if cityi==341301:\n",
    "        print(flows[(cityi,cityj)])\n",
    "    \n",
    "#save to flux matrix\n",
    "matrix = np.zeros([len(nodes), len(nodes)])\n",
    "self_flux = np.zeros(len(nodes))\n",
    "pij1 = np.zeros([len(nodes), len(nodes)])\n",
    "for key,value in flows.items():\n",
    "    id1 = nodes.get(id_city[key[0]],-1)\n",
    "    id2 = nodes.get(id_city[key[1]],-1)\n",
    "    matrix[id1, id2] = value\n",
    "for i in range(matrix.shape[0]):\n",
    "    self_flux[i] = matrix[i, i]\n",
    "    matrix[i, i] = 0\n",
    "    if np.sum(matrix[i,:])>0:\n",
    "        pij1[i,:]=matrix[i,:]/np.sum(matrix[i,:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T00:25:26.858006Z",
     "start_time": "2020-03-17T00:25:26.107333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('./data/Pij_BAIDU.csv',encoding='gbk')\n",
    "df.head(4)\n",
    "\n",
    "cities = {d:i for i,d in enumerate(df['Cities'])}\n",
    "pij2 = np.zeros([len(nodes), len(nodes)])\n",
    "for k,ind in cities.items():\n",
    "    row = df[k]\n",
    "    for city,column in cities.items():\n",
    "        i_indx = nodes.get(city, -1)\n",
    "        if i_indx < 0:\n",
    "            print(city)\n",
    "        j_indx = nodes.get(k, -1)\n",
    "        if j_indx < 0:\n",
    "            print(k)\n",
    "        if i_indx >=0 and j_indx >= 0:\n",
    "            pij2[j_indx, i_indx] = row[column] / 100\n",
    "            if i_indx == j_indx:\n",
    "                pij2[i_indx, j_indx] = 0\n",
    "#pij = pij2\n",
    "\n",
    "bools = pij2 <= 0\n",
    "pij = np.zeros([pij1.shape[0], pij1.shape[0]])\n",
    "for i in range(pij1.shape[0]):\n",
    "    row = pij1[i]\n",
    "    bool1 = bools[i]\n",
    "    values = row * bool1\n",
    "    if np.sum(values) > 0:\n",
    "        ratios = values / np.sum(values)\n",
    "        sum2 = np.sum(pij2[i, :])\n",
    "        pij[i,:] = (1 - sum2) * ratios + pij2[i, :]\n",
    "zeros = np.argwhere(np.sum(pij, axis=1) == 0).reshape(-1)\n",
    "for idx in zeros:\n",
    "    pij[idx][idx] = 1\n",
    "\n",
    "np.sum(pij,1) #Testing normalization\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cases data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T00:25:31.591650Z",
     "start_time": "2020-03-17T00:25:30.494472Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/R_cases_cum.csv',encoding='gbk')\n",
    "df\n",
    "\n",
    "all_cases_cities = list(set(df['city_name']))[1:]\n",
    "\n",
    "wuhan=df.loc[df['city_name']=='武汉市',['confirm','time','heal','dead']]\n",
    "dates = list(wuhan['time'])\n",
    "sorted_dates = np.sort(dates)\n",
    "first_date = datetime.datetime(2020, 1, 1, 0, 0)\n",
    "first_cases = int(wuhan.loc[wuhan['time']=='2020-01-01']['confirm'])\n",
    "\n",
    "\n",
    "all_cases = {}\n",
    "\n",
    "for city in all_cases_cities:\n",
    "    subset = df.loc[df['city_name']==city,['confirm','time','heal','dead']]\n",
    "    zhixia = False\n",
    "    \n",
    "    new_cases = np.array(subset['confirm'])\n",
    "    cued_cases = np.array(subset['heal'])\n",
    "    die_cases = np.array(subset['dead'])\n",
    "    dates = list(subset['time'])\n",
    "    days = []\n",
    "    for i,dd in enumerate(dates):\n",
    "        if pd.isnull(dd):\n",
    "            dd = dates1[i]\n",
    "        if not pd.isnull(dd):\n",
    "            days.append((datetime.datetime.strptime(dd,'%Y-%m-%d') - first_date).days)\n",
    "    sorted_days = np.sort(days)\n",
    "    indx = np.argsort(days)\n",
    "    infected = new_cases[indx]\n",
    "    cued = cued_cases[indx]\n",
    "    death = die_cases[indx]\n",
    "    bools =sorted_days>=0\n",
    "    if len(sorted_days)>0:\n",
    "        all_cases[city] = (sorted_days[bools], infected[bools], cued[bools], death[bools])\n",
    "\n",
    "\n",
    "for case in all_cases.values():\n",
    "    xx = case[0]\n",
    "    yy = case[1]\n",
    "    if len(yy) > 0:\n",
    "        plt.plot(xx,yy,'o-')\n",
    "#plt.semilogy()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SICRD Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model can be described by:\n",
    "\n",
    "$\\partial i_n/\\partial t = \\frac{R_0}{T_L}i_n s_n - \\frac{1}{T_L}i_n + \\gamma \\sum_{m\\neq n}p_{nm}(i_m-i_n)$\n",
    "\n",
    "\n",
    "$\\partial c_n/\\partial t = \\frac{\\alpha}{T_L} c_n - \\frac{1}{T_I} c_n$\n",
    "\n",
    "\n",
    "\n",
    "$\\partial s_n/\\partial t = -\\frac{R_0}{T_L} i_n s_n + \\gamma \\sum_{m\\neq n}p_{nm}(s_m-s_n)$\n",
    "\n",
    "\n",
    "$r_n = 1 - s_n - j_n - l_n$\n",
    "\n",
    "where $i_n$ is infection ratio of city n; \n",
    "$s_n$ is the susceptible ratio of city n; \n",
    "$c_n$ is the confirmed ratio of city n; \n",
    "$R_0$ is the basic reproductive number；\n",
    "$T_I$ is the average time from confirmed to cure / death; \n",
    "$T_L$ is the average time from infected to confirm; \n",
    "$\\gamma$ is the average ratio of migration population to the total population；\n",
    "$p_{nm}$ is the migration ratio from city n to city m.\n",
    "\n",
    "In our simulation, we use matrix and inner product to compute $\\sum_{m\\neq n}p_{nm}(x_m-x_n)$, also with some simplification, i.e., $\\sum_{m\\neq n}p_{nm}(x_m-x_n)=\\sum_m p_{nm}x_m-x_n$ be $P\\cdot \\vec{x} - \\vec{x}$, where $x$ is $l,j,s$, $\\vec{x}=(x_1,x_2,...,x_N)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T01:39:23.131493Z",
     "start_time": "2020-03-17T01:39:23.109412Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "def protect_decay(t, t0, eta, rate_time, relax):\n",
    "    epsilon = 0.001\n",
    "    r = 2 * np.log((1-epsilon)/epsilon) / rate_time\n",
    "    x0 = t0 + rate_time/2\n",
    "    decay = eta / (1 + np.exp(r * (t - x0))) + 1 - eta\n",
    "    if relax>0:\n",
    "        #tstar = x0 + np.log((1-relax*epsilon)/(relax*epsilon+eta-1))\n",
    "        tstar = relax\n",
    "        decay1 = eta/(1 + np.exp(r*(2*tstar - t - x0))) + 1 - eta\n",
    "        decay = decay + decay1\n",
    "    return decay\n",
    "def diff(sicol, t, r_0, t_r, t_l, alpha, gamma, eta, rate_time, protect_day, pijt, intervention, relax=0):\n",
    "    sz = sicol.shape[0] // 3\n",
    "    Is = sicol[:sz] # infected\n",
    "    Cs = sicol[sz:2*sz] # confirmed\n",
    "    ss = sicol[2*sz:] # susceptible\n",
    "    I_term = pijt.dot(Is) - Is\n",
    "    C_term = pijt.dot(Cs) - Cs\n",
    "    S_term = pijt.dot(ss) - ss\n",
    "    cross_term = r_0  * Is * ss / t_l\n",
    "    if intervention:\n",
    "        cross_term = cross_term * protect_decay(t, protect_day, eta, rate_time, relax)\n",
    "        \n",
    "    delta_I = cross_term - Is / t_l + gamma * I_term\n",
    "    delta_C = alpha * Is / t_l - Cs / t_r #+ gamma * C_term\n",
    "    delta_S = - cross_term + gamma * S_term\n",
    "    output = np.r_[delta_I, delta_C, delta_S]\n",
    "    return output\n",
    "# Plot function\n",
    "def plots(prediction,cities,plot_time_span,colors,colors_s):\n",
    "    plt.figure(figsize=(15,10))\n",
    "\n",
    "    for n,(k,i) in enumerate(nodes.items()):\n",
    "        cityname = k\n",
    "        ploty = prediction[:plot_time_span, i + len(nodes)]\n",
    "        plt.semilogy(timespan[:plot_time_span], ploty * city_properties[k]['pop']\n",
    "                     ,'-',alpha=0.1,color=colors[n])\n",
    "        cityname1 = cityname\n",
    "        if cityname[-1]=='市':\n",
    "            cityname1 = cityname[:-1]\n",
    "        itm = all_cases.get(cityname1,[])\n",
    "        if len(itm)==0:\n",
    "            itm = all_cases.get(cityname,[])\n",
    "        if len(itm)>0:\n",
    "            real = itm[1]-itm[2]-itm[3]\n",
    "            #print(real)\n",
    "            #plt.semilogy(itm[0], real, '.',color=colors[n],alpha=0.1)\n",
    "            if k in cities:\n",
    "                #print(itm[0])\n",
    "                #print(real)\n",
    "                c = cities.index(k)\n",
    "                ploty =  prediction[:plot_time_span, i + len(nodes)]\n",
    "                \n",
    "                plt.semilogy(timespan[:plot_time_span], ploty * city_properties[k]['pop']\n",
    "                             ,'-',color=colors_s[c])\n",
    "                plt.semilogy(itm[0], real, 'o',color=colors_s[c])\n",
    "    plt.show()\n",
    "    \n",
    "# Using NeuralODE to fit data\n",
    "# In no-intervention scenario, three parameters can be optimized\n",
    "# In intervention scenarios, t_m can aIso be optimized\n",
    "\n",
    "class my_parameters(nn.Module):\n",
    "    \n",
    "    def __init__(self, batch_size, pij, t_l, t_r, gamma, t_0=22, t_m=30, eta=1, intervention=False):    \n",
    "        super(my_parameters, self).__init__()\n",
    "        self.batch_size = 1\n",
    "        self.pij = pij\n",
    "        self.t_l = t_l\n",
    "        self.t_r = t_r\n",
    "        self.gamma = gamma\n",
    "        self.t0 = t_0\n",
    "        self.tm = t_m\n",
    "        self.eta = eta\n",
    "        self.intervention = intervention\n",
    "        self.batch_size = batch_size\n",
    "        self.pps = Parameter(torch.rand([batch_size, 4]))\n",
    "        self.pps.data[:, 1] *= 500\n",
    "        self.pps.data[:, 3] *= 100\n",
    "    def form_parameter(self):\n",
    "        r0 = 4 * torch.sigmoid(self.pps) * torch.Tensor([[1,0,0,0]]).repeat(batch_size, 1)\n",
    "        l0 = torch.relu(self.pps) * torch.Tensor([[0,1,0,0]]).repeat(batch_size, 1)\n",
    "        alpha = torch.sigmoid(self.pps) * torch.Tensor([[0,0,1,0]]).repeat(batch_size, 1)\n",
    "        tm = torch.relu(self.pps) * torch.Tensor([[0,0,0,1]]).repeat(batch_size, 1)\n",
    "        self.parameters = r0 + l0 + alpha + tm\n",
    "        return r0+l0+alpha+tm\n",
    "    def protect_decay(self, t, t0, eta, rate_time):\n",
    "        epsilon = 0.001\n",
    "        r = 2 * np.log((1-epsilon)/epsilon) / rate_time\n",
    "        x0 = t0 + rate_time/2\n",
    "        decay = eta / (1 + torch.exp(r * (t.unsqueeze(0).repeat(self.batch_size,1) - x0))) + 1 - eta\n",
    "        return decay\n",
    "    def forward(self, t, y):\n",
    "        sz = y.size()[1] // 3\n",
    "        Is = y[:,:sz]\n",
    "        Cs = y[:,sz:2*sz]\n",
    "        ss = y[:,2*sz:]\n",
    "        I_term = Is @ self.pij.t() - Is\n",
    "        C_term = Cs @ self.pij.t() - Cs\n",
    "        S_term = ss @ self.pij.t() - ss\n",
    "        parameter = self.parameters.unsqueeze(1).repeat(1,sz,1)\n",
    "        cross_term = parameter[:, :, 0] * Is * ss / self.t_l\n",
    "        if self.intervention:\n",
    "            cross_term *= self.protect_decay(t, self.t0, self.eta, parameter[:, :, 3])\n",
    "        delta_I = cross_term - Is / self.t_l + self.gamma * I_term\n",
    "        delta_C = parameter[:, :, 2] * Is /self.t_l - Cs / self.t_r #+ self.gamma * C_term\n",
    "        delta_S = - cross_term + self.gamma * S_term\n",
    "        output = torch.cat((delta_I, delta_C, delta_S),1)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T00:25:35.554486Z",
     "start_time": "2020-03-17T00:25:35.460780Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000, 371])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize training data\n",
    "timespan = np.linspace(0, 200, 1000)\n",
    "interval = 1000 / 200\n",
    "infected = torch.zeros([len(timespan), len(nodes)])\n",
    "recovered = torch.zeros([len(timespan), len(nodes)])\n",
    "mask = torch.zeros([len(timespan), len(nodes)])\n",
    "for city,itm in all_cases.items():\n",
    "    city1 = city\n",
    "    try:\n",
    "        if city[-1] != '市':\n",
    "            city1 = city + 'abs市'\n",
    "        idx = nodes.get(city1, -1)\n",
    "        if idx > 0:\n",
    "            infected[(itm[0]*interval).astype(int), idx] = torch.Tensor(\n",
    "                itm[1] - itm[2] - itm[3])\n",
    "            bools = infected[:, idx] >0\n",
    "            mask[bools, idx] = 1\n",
    "            infected[(itm[0]*interval).astype(int), idx] = infected[(itm[0]*interval).astype(int), idx] / city_properties[city]['pop']\n",
    "            recovered[(itm[0]*interval).astype(int),idx] = torch.Tensor((itm[2] + itm[3]\n",
    "                                                      ) / city_properties[city]['pop'])\n",
    "    except:\n",
    "        continue\n",
    "targets = infected\n",
    "targets.size()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Search parameters\n",
    "\n",
    "See `./train.py`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T00:25:40.860953Z",
     "start_time": "2020-03-17T00:25:40.848644Z"
    }
   },
   "outputs": [],
   "source": [
    "experiments = pd.read_pickle('./parameters/experiments_ti_tr_120_new.pkl')\n",
    "experiments = experiments + pd.read_pickle('./parameters/experiments_ti_tr_120_new_2.pkl')\n",
    "experiments = experiments + pd.read_pickle('./parameters/experiments_ti_tr_120_new_3.pkl')\n",
    "#experiments = experiments + pd.read_pickle('./parameters/experiments_ti_tr_120_new_4.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T00:25:42.621686Z",
     "start_time": "2020-03-17T00:25:42.617308Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(experiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the parameters to CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T00:47:48.132287Z",
     "start_time": "2020-03-16T00:47:48.123964Z"
    }
   },
   "outputs": [],
   "source": [
    "r0s=[]\n",
    "l0s=[]\n",
    "alphas=[]\n",
    "t_is=[]\n",
    "t_ls=[]\n",
    "gammas=[]\n",
    "losses=[]\n",
    "for best_para in experiments:\n",
    "    #展示找出来的最优的10个解\n",
    "    fit_param = sorted([(vvv[1],i) for i,vvv in enumerate(best_para)])\n",
    "    itm = best_para[fit_param[0][1]]\n",
    "    #print('r0: {:2.2f}'.format(itm[0][0][0].item()), 'l0:{:2.2f}'.format(itm[0][0][1].item()), \n",
    "    #      'alpha:{:2.2f}'.format(itm[0][0][2].item()), 't_i:{:2.2f}'.format(itm[2][0]),\n",
    "    #      't_r:{:2.2f}'.format(itm[2][1]), 'gamma:{:2.5f}'.format(itm[2][2]))\n",
    "    r0s.append(itm[0][0][0].item())\n",
    "    l0s.append(itm[0][0][1].item())\n",
    "    alphas.append(itm[0][0][2].item())\n",
    "    t_is.append(itm[2][0])\n",
    "    t_ls.append(itm[2][1])\n",
    "    gammas.append(itm[2][2])\n",
    "    losses.append(fit_param[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T00:48:17.208780Z",
     "start_time": "2020-03-16T00:48:17.199808Z"
    }
   },
   "outputs": [],
   "source": [
    "r0s=[]\n",
    "l0s=[]\n",
    "alphas=[]\n",
    "losses=[]\n",
    "exporteddata = []\n",
    "for best_para in experiments:\n",
    "    #展示找出来的最优的120个解\n",
    "    fit_param = sorted([(vvv[1],i) for i,vvv in enumerate(best_para)])\n",
    "    itm = best_para[fit_param[0][1]]\n",
    "    exporteddata.append([itm[0][0][0].item(), itm[0][0][1].item(), \n",
    "          itm[0][0][2].item(), itm[2][0], itm[2][1], itm[2][2]])\n",
    "    #print(itm[0][0][0].item(), itm[0][0][1].item(), \n",
    "    #      itm[0][0][2].item())\n",
    "    r0s.append(itm[0][0][0].item())\n",
    "    l0s.append(itm[0][0][1].item())\n",
    "    alphas.append(itm[0][0][2].item())\n",
    "    losses.append(fit_param[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T03:54:22.352790Z",
     "start_time": "2020-03-15T03:54:22.346860Z"
    }
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "with open('./parameters/experiments_csv.csv', 'w') as csvfile:\n",
    "    writer = csv.writer(csvfile)\n",
    "    writer.writerows(exporteddata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T00:50:00.704762Z",
     "start_time": "2020-03-16T00:50:00.697006Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R0: [2.22, 2.21] (2.15-2.19-2.24-2.35)\n",
      "L0: [404, 411] (276-354-465-497)\n",
      "alpha: [0.91, 0.93] (0.7-0.87-0.96-0.99)\n"
     ]
    }
   ],
   "source": [
    "Report(np.array(exporteddata).T[0], 'R0: ', 2)\n",
    "ReportInt(np.array(exporteddata).T[1], 'L0: ')\n",
    "Report(np.array(exporteddata).T[2], 'alpha: ', 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation and prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Report function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T00:25:45.917021Z",
     "start_time": "2020-03-17T00:25:45.909180Z"
    }
   },
   "outputs": [],
   "source": [
    "# Mean, Median, IQR, and 95% CI\n",
    "def ReportInt(sample, head):\n",
    "    median = int(round(np.median(sample)))\n",
    "    mean = int(round(np.mean(sample)))\n",
    "    low = int(round(np.quantile(sample, 0.025)))\n",
    "    Q1 = int(round(np.quantile(sample, 1/4)))\n",
    "    Q2 = int(round(np.quantile(sample, 3/4)))\n",
    "    high = int(round(np.quantile(sample, 1-0.025)))\n",
    "    \n",
    "    print(head+f'[{mean}, {median}] ({low}-{Q1}-{Q2}-{high})')\n",
    "\n",
    "def Report(sample, head, p):\n",
    "    median = round(np.median(sample), p)\n",
    "    mean = round(np.mean(sample), p)\n",
    "    low = round(np.quantile(sample, 0.025), p)\n",
    "    Q1 = round(np.quantile(sample, 1/4), p)\n",
    "    Q2 = round(np.quantile(sample, 3/4), p)\n",
    "    high = round(np.quantile(sample, 1-0.025), p)\n",
    "    \n",
    "    print(head+f'[{mean}, {median}] ({low}-{Q1}-{Q2}-{high})')\n",
    "    \n",
    "def ReportData(sample, p=2):\n",
    "    mean = round(np.mean(sample), p)\n",
    "    std = np.std(sample)\n",
    "    low = round(np.mean(sample)-1.96*std, p)\n",
    "    high = round(np.mean(sample)+1.96*std, p)\n",
    "    \n",
    "    return [mean, low, high]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Including the total infection population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T00:25:47.977788Z",
     "start_time": "2020-03-17T00:25:47.960550Z"
    }
   },
   "outputs": [],
   "source": [
    "# 不用可微分求解\n",
    "def protect_decay(t, t0, eta, rate_time, relax):\n",
    "    epsilon = 0.001\n",
    "    r = 2 * np.log((1-epsilon)/epsilon) / rate_time\n",
    "    x0 = t0 + rate_time/2\n",
    "    decay = eta / (1 + np.exp(r * (t - x0))) + 1 - eta\n",
    "    if relax>0:\n",
    "        #tstar = x0 + np.log((1-relax*epsilon)/(relax*epsilon+eta-1))\n",
    "        tstar = relax\n",
    "        decay1 = eta/(1 + np.exp(r*(2*tstar - t - x0))) + 1 - eta\n",
    "        decay = decay + decay1\n",
    "    return decay\n",
    "def diff(sicol, t, r_0, t_i, t_l, alpha, gamma, eta, rate_time, protect_day, pijt, intervention, relax=0):\n",
    "    #sicol: 感染，确诊，易感\n",
    "    sz = sicol.shape[0] // 4\n",
    "    Is = sicol[:sz] # 感染\n",
    "    Cs = sicol[sz:2*sz] #确诊\n",
    "    Ss = sicol[2*sz:3*sz] #易感\n",
    "    lterm = pijt.dot(Is) - Is\n",
    "    jterm = pijt.dot(Cs) - Cs\n",
    "    sterm = pijt.dot(Ss) - Ss\n",
    "    cross_term = r_0  * Is * Ss / t_l\n",
    "    if intervention:\n",
    "        cross_term = cross_term * protect_decay(t, protect_day, eta, rate_time, relax)\n",
    "        \n",
    "    delta_l = cross_term - Is / t_l + gamma * lterm\n",
    "    delta_j = alpha * Is / t_l - Cs / t_i #+ gamma * jterm\n",
    "    delta_s = - cross_term + gamma * sterm\n",
    "    \n",
    "    cum_inf_conf = cross_term #infected\n",
    "    #cum_inf_conf = alpha * Is / t_l # confirmed\n",
    "    \n",
    "    output = np.r_[delta_l, delta_j, delta_s, cum_inf_conf]\n",
    "    return output\n",
    "# 特定绘图函数\n",
    "def plots(prediction,cities,plot_time_span,colors,colors_s):\n",
    "    plt.figure(figsize=(15,10))\n",
    "\n",
    "    for n,(k,i) in enumerate(nodes.items()):\n",
    "        cityname = k\n",
    "        ploty = prediction[:plot_time_span, i + len(nodes)]\n",
    "        plt.semilogy(timespan[:plot_time_span], ploty * city_properties[k]['pop']\n",
    "                     ,'-',alpha=0.1,color=colors[n])\n",
    "        cityname1 = cityname\n",
    "        if cityname[-1]=='市':\n",
    "            cityname1 = cityname[:-1]\n",
    "        itm = all_cases.get(cityname1,[])\n",
    "        if len(itm)==0:\n",
    "            itm = all_cases.get(cityname,[])\n",
    "        if len(itm)>0:\n",
    "            real = itm[1]-itm[2]-itm[3]\n",
    "            #print(real)\n",
    "            #plt.semilogy(itm[0], real, '.',color=colors[n],alpha=0.1)\n",
    "            if k in cities:\n",
    "                #print(itm[0])\n",
    "                #print(real)\n",
    "                c = cities.index(k)\n",
    "                ploty =  prediction[:plot_time_span, i + len(nodes)]\n",
    "                \n",
    "                plt.semilogy(timespan[:plot_time_span], ploty * city_properties[k]['pop']\n",
    "                             ,'-',color=colors_s[c])\n",
    "                plt.semilogy(itm[0], real, 'o',color=colors_s[c])\n",
    "    plt.show()\n",
    "    \n",
    "#可微分求微分方程，优化参数\n",
    "#在无intervention时，优化参数有三个，如果考虑intervention，则t_m也可以被优化\n",
    "\n",
    "class my_parameters(nn.Module):\n",
    "    \n",
    "    def __init__(self, batch_size, pij, t_l, t_i, gamma, t_0=22, t_m=30, eta=1, intervention=False):    \n",
    "        super(my_parameters, self).__init__()\n",
    "        self.batch_size = 1\n",
    "        self.pij = pij\n",
    "        self.t_l = t_l\n",
    "        self.t_i = t_i\n",
    "        self.gamma = gamma\n",
    "        self.t0 = t_0\n",
    "        self.tm = t_m\n",
    "        self.eta = eta\n",
    "        self.intervention = intervention\n",
    "        self.batch_size = batch_size\n",
    "        self.pps = Parameter(torch.rand([batch_size, 4]))\n",
    "        self.pps.data[:, 1] *= 500\n",
    "        self.pps.data[:, 3] *= 100\n",
    "    def form_parameter(self):\n",
    "        r0 = 4 * torch.sigmoid(self.pps) * torch.Tensor([[1,0,0,0]]).repeat(batch_size, 1)\n",
    "        l0 = torch.relu(self.pps) * torch.Tensor([[0,1,0,0]]).repeat(batch_size, 1)\n",
    "        alpha = torch.sigmoid(self.pps) * torch.Tensor([[0,0,1,0]]).repeat(batch_size, 1)\n",
    "        tm = torch.relu(self.pps) * torch.Tensor([[0,0,0,1]]).repeat(batch_size, 1)\n",
    "        self.parameters = r0 + l0 + alpha + tm\n",
    "        return r0+l0+alpha+tm\n",
    "    def protect_decay(self, t, t0, eta, rate_time):\n",
    "        epsilon = 0.001\n",
    "        r = 2 * np.log((1-epsilon)/epsilon) / rate_time\n",
    "        x0 = t0 + rate_time/2\n",
    "        decay = eta / (1 + torch.exp(r * (t.unsqueeze(0).repeat(self.batch_size,1) - x0))) + 1 - eta\n",
    "        return decay\n",
    "    def forward(self, t, y):\n",
    "        sz = y.size()[1] // 4\n",
    "        Is = y[:,:sz]\n",
    "        Cs = y[:,sz:2*sz]\n",
    "        Ss = y[:,2*sz:3*sz]\n",
    "        lterm = Is @ self.pij.t() - Is\n",
    "        jterm = Cs @ self.pij.t() - Cs\n",
    "        sterm = Ss @ self.pij.t() - Ss\n",
    "        parameter = self.parameters.unsqueeze(1).repeat(1,sz,1)\n",
    "        cross_term = parameter[:, :, 0] * Is * Ss / self.t_l\n",
    "        if self.intervention:\n",
    "            cross_term *= self.protect_decay(t, self.t0, self.eta, parameter[:, :, 3])\n",
    "        delta_l = cross_term - Is / self.t_l + self.gamma * lterm\n",
    "        delta_j = parameter[:, :, 2] * Is /self.t_l - Cs / self.t_i #+ self.gamma * jterm\n",
    "        delta_s = - cross_term + self.gamma * sterm\n",
    "        output = torch.cat((delta_l, delta_j, delta_s),1)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T01:05:02.513268Z",
     "start_time": "2020-03-17T01:04:44.800231Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wuhan Export (no inter.): [429, 422] (340-402-453-559)\n"
     ]
    }
   ],
   "source": [
    "t_days = 300 #200\n",
    "steps = 1000\n",
    "indexs = list(map(lambda x:int(x/(t_days/steps)), range(t_days)))\n",
    "jan_07 = []\n",
    "export = []\n",
    "cum_all = []\n",
    "cum_death = []\n",
    "\n",
    "wuhan=[]\n",
    "huanggang=[]\n",
    "chongqing=[]\n",
    "shanghai=[]\n",
    "wuhan_cum=[]\n",
    "huanggang_cum = []\n",
    "chongqing_cum = []\n",
    "shanghai_cum = []\n",
    "country=[]\n",
    "cum_confirm=[]\n",
    "death_ratio = 0.023\n",
    "testmedian = False\n",
    "\n",
    "all_inf = []\n",
    "\n",
    "\n",
    "wuhan25=[]\n",
    "wuhan25con=[]\n",
    "beijing25=[]\n",
    "shanghai25=[]\n",
    "shanghai25con=[]\n",
    "guangzhou25=[]\n",
    "shenzhen25=[]\n",
    "chongqing25=[]\n",
    "# Mar 4\n",
    "wuhan4=[]\n",
    "beijing4=[]\n",
    "shanghai4=[]\n",
    "guangzhou4=[]\n",
    "\n",
    "day90=[]\n",
    "\n",
    "city_cum = []\n",
    "\n",
    "MAEs = []\n",
    "all_para_results = []\n",
    "\n",
    "scenarios = {'no_inter':[35,22,False], \\\n",
    "             'baseline':[35,22,True], \\\n",
    "             'early':[35,12,True],\\\n",
    "             'severe':[70,22,True],\\\n",
    "             'new':[28, 22, True],\\\n",
    "             'early_slow':[70, 12, True]\n",
    "            }\n",
    "\n",
    "for best_para in experiments:\n",
    "    fit_param = sorted([(vvv[1],i) for i,vvv in enumerate(best_para)])\n",
    "    itm = best_para[fit_param[0][1]]\n",
    "    r0 = itm[0][0][0].item()\n",
    "    initial_latent = itm[0][0][1].item()\n",
    "    alpha = itm[0][0][2].item()\n",
    "    t_i = itm[2][0]#10#1/0.0134#6 #serial interval，参考SARS\n",
    "    t_l = itm[2][1]#8.89 #一个病患潜伏期时间，参考SARS、MERS\n",
    "    gamma = itm[2][2]#0.03#flowing_ratio\n",
    "    eta = 1\n",
    "    t_m, t_0, interQ = scenarios['early_slow']\n",
    "    interval = t_days / steps\n",
    "    Jan25=round((25-1)/interval)\n",
    "    Mar4=round((64-1)/interval)\n",
    "    \n",
    "    if testmedian:\n",
    "        r0=2.21\n",
    "        initial_latent=390\n",
    "        alpha=0.90\n",
    "\n",
    "\n",
    "    timespan = np.linspace(0, t_days, steps)\n",
    "    inter = timespan[1]-timespan[0]\n",
    "    Is0 = np.zeros(len(nodes))\n",
    "    Cs0 = np.zeros(len(nodes))\n",
    "    Ss0 = np.ones(len(nodes))\n",
    "    Is0[nodes['武汉市']] = float(initial_latent)/float(city_properties['武汉市']['pop'])#1e-4\n",
    "    Cs0[nodes['武汉市']] = float(first_cases)/float(city_properties['武汉市']['pop'])\n",
    "    Ss0[nodes['武汉市']] = 1-Is0[nodes['武汉市']]-Cs0[nodes['武汉市']]\n",
    "    cum_inf_conf = np.zeros(len(nodes))\n",
    "\n",
    "\n",
    "    result = odeint(diff, np.r_[Is0,Cs0,Ss0, cum_inf_conf], timespan, args = (r0, t_i, t_l, alpha, gamma, eta, \n",
    "                                                             t_m, t_0, pij,interQ,100))\n",
    "    #MAEs.append(MAE(result))\n",
    "    all_para_results.append(result[indexs])\n",
    "    jan_07.append(city_properties['武汉市']['pop']*(result[int(6 / interval), len(nodes) + nodes['武汉市']] \n",
    "                  + result[int(6 / interval), nodes['武汉市']]))\n",
    "    wuhanout=np.sum(result[:int(t_0/interval),nodes['武汉市']])*city_properties['武汉市']['pop']*gamma*interval\n",
    "    \n",
    "    city_cum.append(result[-1, 3*len(nodes):])\n",
    "    inflow = 0\n",
    "    for city,v in city_properties.items():\n",
    "        if city!='武汉市':\n",
    "            inflow+=np.sum(result[:int(t_0/interval),nodes[city]])*city_properties[city]['pop']*pij[nodes[city], nodes['武汉市']]*gamma*interval\n",
    "    export.append(wuhanout - inflow)\n",
    "    all_pop = np.zeros([result.shape[0]])\n",
    "    all_confirmed = np.zeros([result.shape[0]])\n",
    "    all_confirmed90 = 0\n",
    "    all_recovery = np.zeros([result.shape[0]])\n",
    "    all_sus = np.zeros([result.shape[0]])\n",
    "    inf_total = np.zeros([result.shape[0]])\n",
    "    for k,city in city_properties.items():\n",
    "        idx = nodes[k]\n",
    "        all_pop += (result[:, idx]+result[:, len(nodes)+idx]) * city['pop']\n",
    "        all_confirmed += (result[:, len(nodes)+idx]) * city['pop']\n",
    "        all_confirmed90 += (result[int(95/interval), len(nodes)+idx]) * city['pop']\n",
    "        all_recovery += (1-result[:, len(nodes)+idx]-result[:,idx]-result[:,2*len(nodes)+idx]) * city['pop']\n",
    "        inf_total += (result[:, idx]) * city['pop']\n",
    "        all_sus +=(result[:, 2*len(nodes)+idx]) #* city['pop']\n",
    "    cum_all.append(np.max(all_recovery)) #总感染\n",
    "    day90.append(all_confirmed90)\n",
    "    #cum_all.append(r0 * np.sum(all_sus * inf_total)*interval/t_l)\n",
    "    cum_death.append(np.max(all_recovery * death_ratio))\n",
    "    cum_confirm.append(alpha * np.sum(inf_total)*interval/t_l) #总确诊\n",
    "    \n",
    "    wuhan.append(np.max((result[:, nodes['武汉市']]+result[:, nodes['武汉市']+len(nodes)])*\n",
    "                        city_properties['武汉市']['pop']))\n",
    "    v=np.amax((result[:, nodes['武汉市']]+result[:, nodes['武汉市']+len(nodes)])*\n",
    "                        city_properties['武汉市']['pop'])\n",
    "    \n",
    "    wuhan_cum.append((1-result[-1, nodes['武汉市']+2*len(nodes)]-result[-1, nodes['武汉市']+1*len(nodes)]-result[-1, nodes['武汉市']+0*len(nodes)]) * city_properties['武汉市']['pop'])\n",
    "    huanggang_cum.append((1-result[-1, nodes['黄冈市']+2*len(nodes)]-result[-1, nodes['黄冈市']+1*len(nodes)]-result[-1, nodes['黄冈市']+0*len(nodes)]) * city_properties['黄冈市']['pop'])\n",
    "    chongqing_cum.append((1-result[-1, nodes['重庆市']+2*len(nodes)]-result[-1, nodes['重庆市']+1*len(nodes)]-result[-1, nodes['重庆市']+0*len(nodes)]) * city_properties['重庆市']['pop'])\n",
    "    shanghai_cum.append((1-result[-1, nodes['上海市']+2*len(nodes)]-result[-1, nodes['上海市']+1*len(nodes)]-result[-1, nodes['上海市']+0*len(nodes)]) * city_properties['上海市']['pop'])\n",
    "    \n",
    "    country.append(np.amax(all_pop))\n",
    "    huanggang.append(np.max((result[:, nodes['黄冈市']]+result[:, nodes['黄冈市']+len(nodes)])*\n",
    "                        city_properties['黄冈市']['pop']))\n",
    "    chongqing.append(np.max((result[:, nodes['重庆市']]+result[:, nodes['重庆市']+len(nodes)])*\n",
    "                        city_properties['重庆市']['pop']))\n",
    "    shanghai.append(np.max((result[:, nodes['上海市']]+result[:, nodes['上海市']+len(nodes)])*\n",
    "                        city_properties['上海市']['pop']))\n",
    "    # 算25号的人数\n",
    "    # 顺序：感染、确诊、易感\n",
    "    wuhan25.append((result[Jan25, nodes['武汉市']]+result[Jan25, nodes['武汉市']+len(nodes)])*\n",
    "                        city_properties['武汉市']['pop'])\n",
    "    wuhan25con.append((result[Jan25, nodes['武汉市']+len(nodes)])*\n",
    "                        city_properties['武汉市']['pop'])\n",
    "    beijing25.append((result[Jan25, nodes['北京市']]+result[Jan25, nodes['北京市']+len(nodes)])*\n",
    "                        city_properties['北京市']['pop'])\n",
    "    shanghai25.append((result[Jan25, nodes['上海市']]+result[Jan25, nodes['上海市']+len(nodes)])*\n",
    "                        city_properties['上海市']['pop'])\n",
    "    shanghai25con.append((result[Jan25, nodes['上海市']+len(nodes)])*\n",
    "                        city_properties['上海市']['pop'])\n",
    "    guangzhou25.append((result[Jan25, nodes['广州市']]+result[Jan25, nodes['广州市']+len(nodes)])*\n",
    "                        city_properties['广州市']['pop'])\n",
    "    shenzhen25.append((result[Jan25, nodes['深圳市']]+result[Jan25, nodes['深圳市']+len(nodes)])*\n",
    "                        city_properties['深圳市']['pop'])\n",
    "    chongqing25.append((result[Jan25, nodes['重庆市']]+result[Jan25, nodes['重庆市']+len(nodes)])*\n",
    "                        city_properties['重庆市']['pop'])\n",
    "    \n",
    "    wuhan4.append((result[Mar4, nodes['武汉市']]+result[Mar4, nodes['武汉市']+len(nodes)])*\n",
    "                        city_properties['武汉市']['pop'])\n",
    "    beijing4.append((result[Mar4, nodes['北京市']]+result[Mar4, nodes['北京市']+len(nodes)])*\n",
    "                        city_properties['北京市']['pop'])\n",
    "    shanghai4.append((result[Mar4, nodes['上海市']]+result[Mar4, nodes['上海市']+len(nodes)])*\n",
    "                        city_properties['上海市']['pop'])\n",
    "    guangzhou4.append((result[Mar4, nodes['广州市']]+result[Mar4, nodes['广州市']+len(nodes)])*\n",
    "                        city_properties['广州市']['pop'])\n",
    "    \n",
    "# 转成目标格式：\n",
    "# 城市 - 时间 - 实验次数\n",
    "plotdata = np.array(all_para_results).transpose(2,1,0)\n",
    "\n",
    "pop = np.zeros(371)\n",
    "for city_name in nodes.keys():\n",
    "    idx = nodes[city_name]\n",
    "    _pop = city_properties[city_name]['pop']\n",
    "    pop[idx] = _pop\n",
    "pop = np.concatenate([pop,pop,pop,pop])\n",
    "\n",
    "for idx in range(len(plotdata)):\n",
    "    plotdata[idx] *= pop[idx]\n",
    "ReportInt(1.56*np.array(export), 'Wuhan Export (no inter.): ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T00:26:43.736156Z",
     "start_time": "2020-03-17T00:26:43.730792Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1484, 400, 360)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T01:05:59.626916Z",
     "start_time": "2020-03-17T01:05:59.007570Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.mean(plotdata[len(nodes):2*len(nodes),:,:],axis=2).T)\n",
    "plt.plot(np.ones(300))\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T01:05:04.909613Z",
     "start_time": "2020-03-17T01:05:04.183413Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.sum(plotdata[:2*len(nodes),:,:],axis=0))\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T00:24:25.831238Z",
     "start_time": "2020-03-16T00:24:25.825316Z"
    }
   },
   "outputs": [],
   "source": [
    "def pltdata(cname, t):\n",
    "    # cname: city name\n",
    "    # t: time\n",
    "    idx = nodes[cname] # index\n",
    "    nl = len(nodes) # node length\n",
    "    popu = city_properties[cname]['pop']\n",
    "    prov = city_properties[cname]['prov']\n",
    "    \n",
    "    # 感染、确诊、易感\n",
    "    # index,cityname,citypopu,cityprov,time,\n",
    "    # infe, infe_l, infe_h,\n",
    "    # conf, conf_l, conf_h,\n",
    "    # susp, susp_l, susp_h\n",
    "    basicdata = [0, cname, popu, prov, t]\n",
    "    infs = ReportData(plotdata[idx, t])\n",
    "    confs = ReportData(plotdata[nl+idx, t])\n",
    "    susps = ReportData(plotdata[2*nl+idx, t])\n",
    "    infconf = ReportData(plotdata[idx, t]+plotdata[nl+idx, t])\n",
    "    return basicdata + infs + confs+susps + infconf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T00:46:49.703753Z",
     "start_time": "2020-03-16T00:46:49.653795Z"
    }
   },
   "outputs": [],
   "source": [
    "maes=[]\n",
    "mapes=[]\n",
    "\n",
    "for city,i in nodes.items():\n",
    "    itm = all_cases.get(city, [])\n",
    "    if len(itm)==0:\n",
    "        itm = all_cases.get(city[:-1], [])\n",
    "    if len(itm)>0:\n",
    "        times = itm[0]\n",
    "        confirmed = itm[1] - itm[2] - itm[3]\n",
    "        mae = []\n",
    "        mape = []\n",
    "        count = 0\n",
    "        for j,time in enumerate(times):\n",
    "            real = confirmed[j]\n",
    "            if real > 0:\n",
    "                predict = result[int(time / interval), len(nodes)+i] * city_properties[city]['pop']\n",
    "                mae.append(np.abs(real - predict))\n",
    "                mape.append(np.abs(real - predict) / real)\n",
    "                count += 1\n",
    "                #print(mape)\n",
    "                if real == 0:\n",
    "                    print(itm)\n",
    "                    print(confirmed[j])\n",
    "                    print(j)\n",
    "        if count > 0:\n",
    "            maes.append(np.mean(mae))\n",
    "            mapes.append(np.mean(mape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-14T19:17:08.001940Z",
     "start_time": "2020-03-14T19:17:07.841346Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.log10(maes))\n",
    "#plt.hist(mapes)\n",
    "plt.xlabel('$\\log_{10}(MAE)$')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T00:46:58.429755Z",
     "start_time": "2020-03-16T00:46:58.424682Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16.5716026154312, 6.573720206900261)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(maes), np.std(maes) / np.sqrt(len(nodes)) * 1.96"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confirmed population at different days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T00:41:53.939108Z",
     "start_time": "2020-03-16T00:41:53.927959Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 ========\n",
      "wuhan: [521, 503] (325-421-605-796)\n",
      "shanghai: [1.29, 1.31] (0.99-1.29-1.33-1.37)\n",
      "China: [646, 631] (452-548-734-893)\n",
      "17 ========\n",
      "wuhan: [1330, 1247] (724-1005-1564-2433)\n",
      "shanghai: [7.6, 7.66] (6.67-7.58-7.73-7.86)\n",
      "China: [2032, 1957] (1381-1693-2294-3058)\n",
      "24 ========\n",
      "wuhan: [3244, 2936] (1558-2281-3817-7129)\n",
      "shanghai: [31.72, 31.81] (29.81-31.09-32.48-33.15)\n",
      "China: [6038, 5740] (4015-4948-6766-10074)\n",
      "31 ========\n",
      "wuhan: [7906, 6833] (3314-5148-9222-20294)\n",
      "shanghai: [114.75, 114.22] (102.93-109.76-119.29-129.28)\n",
      "China: [17575, 16408] (11251-14078-19535-32420)\n",
      "38 ========\n",
      "wuhan: [18091, 14883] (6691-10913-20719-52415)\n",
      "shanghai: [356.43, 351.14] (301.31-331.75-372.0-454.46)\n",
      "China: [46948, 43108] (28917-36602-51760-93492)\n"
     ]
    }
   ],
   "source": [
    "# 确诊人数\n",
    "for tt in [10, 17, 24, 31, 38]:\n",
    "    print(tt, '========')\n",
    "    #tt=10# Feb 7: 38\n",
    "    ReportInt(plotdata[len(nodes)+nodes['武汉市'], tt-1], 'wuhan: ')\n",
    "    Report(plotdata[len(nodes)+nodes['上海市'], tt-1], 'shanghai: ', 2)\n",
    "    ReportInt(np.sum(plotdata[len(nodes):2*len(nodes), tt-1], axis=0), 'China: ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export to as the plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T04:53:17.711651Z",
     "start_time": "2020-03-16T04:53:17.692757Z"
    }
   },
   "outputs": [],
   "source": [
    "title=['index','cityname','citypopu','cityprov','time',\n",
    "        'infe', 'infe_l', 'infe_h',\n",
    "        'conf', 'conf_l', 'conf_h',\n",
    "        'susp', 'susp_l', 'susp_h',\n",
    "        'inf+conf', 'inf+conf_l', 'inf+conf_h',\n",
    "      ]\n",
    "\n",
    "exportplotdata = []\n",
    "exportplotdata.append(title)\n",
    "for cname in nodes.keys():\n",
    "    for t in range(200):\n",
    "        #print(cname)\n",
    "        #print(pltdata(cname, t))\n",
    "        exportplotdata.append(pltdata(cname, t))\n",
    "for i in range(len(exportplotdata) - 1):\n",
    "    exportplotdata[i+1][0] = i\n",
    "\n",
    "import csv\n",
    "\n",
    "with open('./data/plot_data_early_slow_cities.csv', 'w') as csvfile:\n",
    "    writer = csv.writer(csvfile)\n",
    "    writer.writerows(exportplotdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### no intervention:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T23:31:46.887366Z",
     "start_time": "2020-03-16T23:31:46.859780Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wuhan Jan 7: [1161, 1138] (733-972-1347-1675)\n",
      "wuhan\t\t [3606740, 3603909] (3149806-3476867-3729595-4057082)\n",
      "huanggang\t [2086858, 2086091] (1824794-2014948-2156590-2341654)\n",
      "chongqing\t [10333431, 10341738] (9064714-9990037-10674626-11554892)\n",
      "shanghai\t [8145281, 8149047] (7147801-7872539-8412533-9117323)\n",
      "total infect\t [1182444865, 1180451880] (1160072370-1173376730-1188786380-1220233414)\n",
      "total infect\t [1.1824, 1.1805] (1.1601-1.1734-1.1888-1.2202)\n",
      "country max\t [460187299, 461361443] (404797776-445304125-475093540-508583304)\n",
      "total death\t [27196232, 27150393] (26681665-26987665-27342087-28065369)\n",
      "total death\t [0.0272, 0.0272] (0.0267-0.027-0.0273-0.0281)\n",
      "total confirm\t [1085171349, 1117449838] (845380912-1037069808-1156169240-1195085060)\n",
      "total confirm\t [1.0852, 1.1174] (0.8454-1.0371-1.1562-1.1951)\n",
      "Jan 25:=====\n",
      "Wuhan: [11581, 10318] (5111-8219-13513-25997)\n",
      "Shanghai: [144, 143] (127-137-149-170)\n",
      "Beijing: [112, 112] (97-106-116-130)\n",
      "Guangzhou: [79, 77] (71-75-82-97)\n",
      "Mar 4:======\n",
      "Wuhan: [1232874, 1076525] (458849-797533-1463893-3276767)\n",
      "Shanghai: [92879, 86360] (62974-78163-96431-169120)\n",
      "Beijing: [68708, 63486] (45737-56905-71289-129237)\n",
      "Guangzhou: [58207, 55103] (42170-50684-60121-96755)\n",
      "Total inf:=====\n",
      "Wuhan Total Inf.: [11219977, 11225900] (11017019-11155250-11292152-11406615)\n",
      "total death: [258059, 258196] (253391-256571-259720-262352)\n",
      "Huanggang Total Inf.: [6169650, 6192839] (5856919-6060205-6279568-6413495)\n",
      "total death: [141901.95, 142435.3] (134709.13-139384.72-144430.06-147510.38)\n",
      "Chongqing Total Inf.: [27062522, 27043198] (26440281-26841893-27252828-27896624)\n",
      "total death: [622438.0, 621993.56] (608126.46-617363.55-626815.04-641622.34)\n",
      "Shanghai Total Inf.: [21065353, 21041185] (20715874-20934391-21183761-21593042)\n",
      "total death: [484503.11, 483947.25] (476465.11-481490.99-487226.49-496639.96)\n"
     ]
    }
   ],
   "source": [
    "v=jan_07\n",
    "ReportInt(v, 'wuhan Jan 7: ')\n",
    "\n",
    "v = wuhan\n",
    "ReportInt(v, 'wuhan\\t\\t ')\n",
    "\n",
    "v = huanggang\n",
    "ReportInt(v, 'huanggang\\t ')\n",
    "\n",
    "v = chongqing\n",
    "ReportInt(v, 'chongqing\\t ')\n",
    "\n",
    "v = shanghai\n",
    "ReportInt(v, 'shanghai\\t ')\n",
    "\n",
    "v = cum_all\n",
    "ReportInt(v, 'total infect\\t ')\n",
    "Report(np.array(v)/1e9, 'total infect\\t ',4)\n",
    "\n",
    "v = country\n",
    "ReportInt(v, 'country max\\t ')\n",
    "\n",
    "v = cum_death\n",
    "ReportInt(v, 'total death\\t ')\n",
    "Report(np.array(v)/1e9, 'total death\\t ',4)\n",
    "\n",
    "v = cum_confirm\n",
    "ReportInt(v, 'total confirm\\t ')\n",
    "Report(np.array(v)/1e9, 'total confirm\\t ',4)\n",
    "\n",
    "print('Jan 25:=====')\n",
    "# Jan. 25, 2020\n",
    "v = wuhan25\n",
    "ReportInt(v, 'Wuhan: ')\n",
    "\n",
    "v = shanghai25\n",
    "ReportInt(v, 'Shanghai: ')\n",
    "\n",
    "v = beijing25\n",
    "ReportInt(v, 'Beijing: ')\n",
    "\n",
    "v = guangzhou25\n",
    "ReportInt(v, 'Guangzhou: ')\n",
    "\n",
    "\n",
    "print('Mar 4:======')\n",
    "v = wuhan4\n",
    "ReportInt(v, 'Wuhan: ')\n",
    "\n",
    "v = shanghai4\n",
    "ReportInt(v, 'Shanghai: ')\n",
    "\n",
    "v = beijing4\n",
    "ReportInt(v, 'Beijing: ')\n",
    "\n",
    "v = guangzhou4\n",
    "ReportInt(v, 'Guangzhou: ')\n",
    "\n",
    "\n",
    "print('Total inf:=====')\n",
    "v = np.array(city_cum)[:, nodes['武汉市']]*city_properties['武汉市']['pop']\n",
    "ReportInt(v, 'Wuhan Total Inf.: ')\n",
    "ReportInt(v*death_ratio, 'total death: ')\n",
    "\n",
    "v = np.array(city_cum)[:, nodes['黄冈市']]*city_properties['黄冈市']['pop']\n",
    "ReportInt(v, 'Huanggang Total Inf.: ')\n",
    "Report(v*death_ratio, 'total death: ',2)\n",
    "\n",
    "v = np.array(city_cum)[:, nodes['重庆市']]*city_properties['重庆市']['pop']\n",
    "ReportInt(v, 'Chongqing Total Inf.: ')\n",
    "Report(v*death_ratio, 'total death: ',2)\n",
    "\n",
    "v = np.array(city_cum)[:, nodes['上海市']]*city_properties['上海市']['pop']\n",
    "ReportInt(v, 'Shanghai Total Inf.: ')\n",
    "Report(v*death_ratio, 'total death: ',2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T01:54:10.496578Z",
     "start_time": "2020-03-15T01:54:10.492420Z"
    }
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "def savelist(filename, dlist):\n",
    "    with open('./data/'+filename, 'w') as csvfile:\n",
    "        writer = csv.writer(csvfile)\n",
    "        writer.writerows(dlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T01:57:43.878175Z",
     "start_time": "2020-03-15T01:57:43.872667Z"
    }
   },
   "outputs": [],
   "source": [
    "savelist('wuhan_max_no_inter.csv', [wuhan])\n",
    "savelist('all_inf_no_inter.csv', [cum_all])\n",
    "savelist('wuhan_25_no_inter.csv', [wuhan25])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### baseline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T01:58:25.478619Z",
     "start_time": "2020-03-15T01:58:25.474395Z"
    }
   },
   "outputs": [],
   "source": [
    "savelist('wuhan_max_base.csv', [wuhan])\n",
    "savelist('all_inf_no_base.csv', [cum_all])\n",
    "savelist('wuhan_25_no_base.csv', [wuhan25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T00:43:53.998484Z",
     "start_time": "2020-03-16T00:43:53.970021Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wuhan Jan 7: [1161, 1138] (733-972-1347-1675)\n",
      "wuhan\t\t [59772, 47914] (20537-36089-66658-176147)\n",
      "huanggang\t [13117, 12543] (7464-10680-14733-22938)\n",
      "chongqing\t [1772, 1751] (1423-1631-1864-2251)\n",
      "shanghai\t [1541, 1521] (1234-1414-1624-1977)\n",
      "total infect\t [302944, 278137] (173553-235491-336302-604469)\n",
      "total infect\t [0.0003, 0.0003] (0.0002-0.0002-0.0003-0.0006)\n",
      "country max\t [178887, 164768] (106777-141930-194832-348530)\n",
      "total death\t [6968, 6397] (3992-5416-7735-13903)\n",
      "total death\t [0.0, 0.0] (0.0-0.0-0.0-0.0)\n",
      "total confirm\t [255694, 232676] (154390-196810-280209-522720)\n",
      "total confirm\t [0.0003, 0.0002] (0.0002-0.0002-0.0003-0.0005)\n",
      "Jan 25:=====\n",
      "Wuhan: [11566, 10306] (5104-8209-13497-25963)\n",
      "Shanghai: [144, 143] (127-137-149-170)\n",
      "Beijing: [111, 111] (97-106-116-130)\n",
      "Guangzhou: [79, 77] (71-75-82-96)\n",
      "Mar 4:======\n",
      "Wuhan: [12021, 9146] (3791-6498-13270-36072)\n",
      "Shanghai: [448, 435] (336-401-478-648)\n",
      "Beijing: [334, 323] (245-295-357-500)\n",
      "Guangzhou: [276, 272] (220-254-292-360)\n",
      "Total inf:=====\n",
      "Wuhan Total Inf.: [106663, 88491] (39670-68234-121736-289773)\n",
      "total death: [2453, 2035] (912-1569-2800-6665)\n",
      "Huanggang Total Inf.: [19560, 18984] (12079-16353-21838-31349)\n",
      "total death: [449.87, 436.63] (277.82-376.12-502.28-721.02)\n",
      "Chongqing Total Inf.: [2411, 2376] (1939-2199-2566-3195)\n",
      "total death: [55.46, 54.66] (44.6-50.59-59.02-73.49)\n",
      "Shanghai Total Inf.: [2103, 2072] (1683-1912-2238-2815)\n",
      "total death: [48.36, 47.65] (38.7-43.97-51.48-64.74)\n"
     ]
    }
   ],
   "source": [
    "v=jan_07\n",
    "ReportInt(v, 'wuhan Jan 7: ')\n",
    "\n",
    "v = wuhan\n",
    "ReportInt(v, 'wuhan\\t\\t ')\n",
    "\n",
    "v = huanggang\n",
    "ReportInt(v, 'huanggang\\t ')\n",
    "\n",
    "v = chongqing\n",
    "ReportInt(v, 'chongqing\\t ')\n",
    "\n",
    "v = shanghai\n",
    "ReportInt(v, 'shanghai\\t ')\n",
    "\n",
    "v = cum_all\n",
    "ReportInt(v, 'total infect\\t ')\n",
    "Report(np.array(v)/1e9, 'total infect\\t ',4)\n",
    "\n",
    "v = country\n",
    "ReportInt(v, 'country max\\t ')\n",
    "\n",
    "v = cum_death\n",
    "ReportInt(v, 'total death\\t ')\n",
    "Report(np.array(v)/1e9, 'total death\\t ',4)\n",
    "\n",
    "v = cum_confirm\n",
    "ReportInt(v, 'total confirm\\t ')\n",
    "Report(np.array(v)/1e9, 'total confirm\\t ',4)\n",
    "\n",
    "print('Jan 25:=====')\n",
    "# Jan. 25, 2020\n",
    "v = wuhan25\n",
    "ReportInt(v, 'Wuhan: ')\n",
    "\n",
    "v = shanghai25\n",
    "ReportInt(v, 'Shanghai: ')\n",
    "\n",
    "v = beijing25\n",
    "ReportInt(v, 'Beijing: ')\n",
    "\n",
    "v = guangzhou25\n",
    "ReportInt(v, 'Guangzhou: ')\n",
    "\n",
    "\n",
    "print('Mar 4:======')\n",
    "v = wuhan4\n",
    "ReportInt(v, 'Wuhan: ')\n",
    "\n",
    "v = shanghai4\n",
    "ReportInt(v, 'Shanghai: ')\n",
    "\n",
    "v = beijing4\n",
    "ReportInt(v, 'Beijing: ')\n",
    "\n",
    "v = guangzhou4\n",
    "ReportInt(v, 'Guangzhou: ')\n",
    "\n",
    "\n",
    "print('Total inf:=====')\n",
    "v = np.array(city_cum)[:, nodes['武汉市']]*city_properties['武汉市']['pop']\n",
    "ReportInt(v, 'Wuhan Total Inf.: ')\n",
    "ReportInt(v*death_ratio, 'total death: ')\n",
    "\n",
    "v = np.array(city_cum)[:, nodes['黄冈市']]*city_properties['黄冈市']['pop']\n",
    "ReportInt(v, 'Huanggang Total Inf.: ')\n",
    "Report(v*death_ratio, 'total death: ',2)\n",
    "\n",
    "v = np.array(city_cum)[:, nodes['重庆市']]*city_properties['重庆市']['pop']\n",
    "ReportInt(v, 'Chongqing Total Inf.: ')\n",
    "Report(v*death_ratio, 'total death: ',2)\n",
    "\n",
    "v = np.array(city_cum)[:, nodes['上海市']]*city_properties['上海市']['pop']\n",
    "ReportInt(v, 'Shanghai Total Inf.: ')\n",
    "Report(v*death_ratio, 'total death: ',2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### early warning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T00:56:26.673249Z",
     "start_time": "2020-03-16T00:56:26.645836Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wuhan Jan 7: [1161, 1138] (733-972-1347-1675)\n",
      "wuhan\t\t [16293, 13977] (6535-10911-18802-40437)\n",
      "huanggang\t [2792, 2778] (1874-2450-3106-3868)\n",
      "chongqing\t [326, 327] (280-309-342-377)\n",
      "shanghai\t [285, 286] (243-269-299-334)\n",
      "total infect\t [68307, 64756] (41270-54964-76852-120930)\n",
      "total infect\t [0.0001, 0.0001] (0.0-0.0001-0.0001-0.0001)\n",
      "country max\t [39361, 37196] (24953-32401-43643-67985)\n",
      "total death\t [1571, 1489] (949-1264-1768-2781)\n",
      "total death\t [0.0, 0.0] (0.0-0.0-0.0-0.0)\n",
      "total confirm\t [55971, 52254] (35596-44998-62088-101563)\n",
      "total confirm\t [0.0001, 0.0001] (0.0-0.0-0.0001-0.0001)\n",
      "Jan 25:=====\n",
      "Wuhan: [10908, 9727] (4820-7756-12740-24381)\n",
      "Shanghai: [135, 134] (120-129-140-159)\n",
      "Beijing: [105, 105] (91-100-109-122)\n",
      "Guangzhou: [74, 73] (67-71-77-91)\n",
      "Mar 4:======\n",
      "Wuhan: [1229, 1007] (424-719-1418-3123)\n",
      "Shanghai: [39, 38] (29-35-42-50)\n",
      "Beijing: [29, 29] (21-26-31-38)\n",
      "Guangzhou: [24, 23] (19-22-25-29)\n",
      "Total inf:=====\n",
      "Wuhan Total Inf.: [29006, 25759] (12575-20378-33878-65794)\n",
      "total death: [667, 592] (289-469-779-1513)\n",
      "Huanggang Total Inf.: [3861, 3880] (2748-3438-4286-5044)\n",
      "total death: [88.81, 89.23] (63.2-79.06-98.59-116.01)\n",
      "Chongqing Total Inf.: [411, 404] (352-384-431-510)\n",
      "total death: [9.44, 9.29] (8.09-8.84-9.91-11.72)\n",
      "Shanghai Total Inf.: [360, 356] (306-336-379-446)\n",
      "total death: [8.28, 8.18] (7.04-7.73-8.72-10.25)\n"
     ]
    }
   ],
   "source": [
    "v=jan_07\n",
    "ReportInt(v, 'wuhan Jan 7: ')\n",
    "\n",
    "v = wuhan\n",
    "ReportInt(v, 'wuhan\\t\\t ')\n",
    "\n",
    "v = huanggang\n",
    "ReportInt(v, 'huanggang\\t ')\n",
    "\n",
    "v = chongqing\n",
    "ReportInt(v, 'chongqing\\t ')\n",
    "\n",
    "v = shanghai\n",
    "ReportInt(v, 'shanghai\\t ')\n",
    "\n",
    "v = cum_all\n",
    "ReportInt(v, 'total infect\\t ')\n",
    "Report(np.array(v)/1e9, 'total infect\\t ',4)\n",
    "\n",
    "v = country\n",
    "ReportInt(v, 'country max\\t ')\n",
    "\n",
    "v = cum_death\n",
    "ReportInt(v, 'total death\\t ')\n",
    "Report(np.array(v)/1e9, 'total death\\t ',4)\n",
    "\n",
    "v = cum_confirm\n",
    "ReportInt(v, 'total confirm\\t ')\n",
    "Report(np.array(v)/1e9, 'total confirm\\t ',4)\n",
    "\n",
    "print('Jan 25:=====')\n",
    "# Jan. 25, 2020\n",
    "v = wuhan25\n",
    "ReportInt(v, 'Wuhan: ')\n",
    "\n",
    "v = shanghai25\n",
    "ReportInt(v, 'Shanghai: ')\n",
    "\n",
    "v = beijing25\n",
    "ReportInt(v, 'Beijing: ')\n",
    "\n",
    "v = guangzhou25\n",
    "ReportInt(v, 'Guangzhou: ')\n",
    "\n",
    "\n",
    "print('Mar 4:======')\n",
    "v = wuhan4\n",
    "ReportInt(v, 'Wuhan: ')\n",
    "\n",
    "v = shanghai4\n",
    "ReportInt(v, 'Shanghai: ')\n",
    "\n",
    "v = beijing4\n",
    "ReportInt(v, 'Beijing: ')\n",
    "\n",
    "v = guangzhou4\n",
    "ReportInt(v, 'Guangzhou: ')\n",
    "\n",
    "\n",
    "print('Total inf:=====')\n",
    "v = np.array(city_cum)[:, nodes['武汉市']]*city_properties['武汉市']['pop']\n",
    "ReportInt(v, 'Wuhan Total Inf.: ')\n",
    "ReportInt(v*death_ratio, 'total death: ')\n",
    "\n",
    "v = np.array(city_cum)[:, nodes['黄冈市']]*city_properties['黄冈市']['pop']\n",
    "ReportInt(v, 'Huanggang Total Inf.: ')\n",
    "Report(v*death_ratio, 'total death: ',2)\n",
    "\n",
    "v = np.array(city_cum)[:, nodes['重庆市']]*city_properties['重庆市']['pop']\n",
    "ReportInt(v, 'Chongqing Total Inf.: ')\n",
    "Report(v*death_ratio, 'total death: ',2)\n",
    "\n",
    "v = np.array(city_cum)[:, nodes['上海市']]*city_properties['上海市']['pop']\n",
    "ReportInt(v, 'Shanghai Total Inf.: ')\n",
    "Report(v*death_ratio, 'total death: ',2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### servere:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T03:32:55.011457Z",
     "start_time": "2020-03-15T03:32:54.986154Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wuhan Jan 7: [1170, 1143] (758-982-1361-1675)\n",
      "wuhan\t\t [358063, 271337] (118579-195916-397939-1150361)\n",
      "huanggang\t [113152, 99041] (54697-80486-125586-256793)\n",
      "chongqing\t [20112, 19163] (14121-17254-20949-32491)\n",
      "shanghai\t [17479, 16652] (12323-15004-18158-28310)\n",
      "total infect\t [3032531, 2738222] (1681473-2306353-3283727-6512208)\n",
      "total infect\t [3.0325, 2.7382] (1.6815-2.3064-3.2837-6.5122)\n",
      "country max\t [1585423, 1404554] (923650-1206336-1699167-3355589)\n",
      "total death\t [69748, 62979] (38674-53046-75526-149781)\n",
      "total death\t [0.0697, 0.063] (0.0387-0.053-0.0755-0.1498)\n",
      "total confirm\t [2658018, 2320576] (1563309-1950397-2849074-5932460)\n",
      "total confirm\t [2.658, 2.3206] (1.5633-1.9504-2.8491-5.9325)\n",
      "Jan 25:=====\n",
      "Wuhan: [11763, 10358] (5474-8246-13680-25949)\n",
      "Shanghai: [144, 143] (128-137-149-171)\n",
      "Beijing: [112, 112] (97-106-116-131)\n",
      "Guangzhou: [79, 77] (71-75-82-97)\n",
      "Mar 4:======\n",
      "Wuhan: [321910, 243680] (106609-175335-360745-1031804)\n",
      "Shanghai: [17122, 16234] (12011-14632-17797-27877)\n",
      "Beijing: [12678, 11932] (8720-10696-13244-21326)\n",
      "Guangzhou: [10691, 10301] (7945-9436-11104-15914)\n",
      "Total inf:=====\n",
      "Wuhan Total Inf.: [743569, 578794] (264189-440235-824096-2253648)\n",
      "total death: [17102, 13312] (6076-10125-18954-51834)\n",
      "Huanggang Total Inf.: [208664, 188927] (103708-156132-231962-440543)\n",
      "total death: [4799, 4345] (2385-3591-5335-10132)\n",
      "Chongqing Total Inf.: [34029, 32585] (24029-29125-35813-52750)\n",
      "total death: [783, 749] (553-670-824-1213)\n",
      "Shanghai Total Inf.: [29582, 28257] (21015-25267-31064-46127)\n",
      "total death: [680, 650] (483-581-714-1061)\n"
     ]
    }
   ],
   "source": [
    "v=jan_07\n",
    "ReportInt(v, 'wuhan Jan 7: ')\n",
    "\n",
    "v = wuhan\n",
    "ReportInt(v, 'wuhan\\t\\t ')\n",
    "\n",
    "v = huanggang\n",
    "ReportInt(v, 'huanggang\\t ')\n",
    "\n",
    "v = chongqing\n",
    "ReportInt(v, 'chongqing\\t ')\n",
    "\n",
    "v = shanghai\n",
    "ReportInt(v, 'shanghai\\t ')\n",
    "\n",
    "v = cum_all\n",
    "ReportInt(v, 'total infect\\t ')\n",
    "Report(np.array(v)/1e6, 'total infect\\t ',4)\n",
    "\n",
    "v = country\n",
    "ReportInt(v, 'country max\\t ')\n",
    "\n",
    "v = cum_death\n",
    "ReportInt(v, 'total death\\t ')\n",
    "Report(np.array(v)/1e6, 'total death\\t ',4)\n",
    "\n",
    "v = cum_confirm\n",
    "ReportInt(v, 'total confirm\\t ')\n",
    "Report(np.array(v)/1e6, 'total confirm\\t ',4)\n",
    "\n",
    "print('Jan 25:=====')\n",
    "# Jan. 25, 2020\n",
    "v = wuhan25\n",
    "ReportInt(v, 'Wuhan: ')\n",
    "\n",
    "v = shanghai25\n",
    "ReportInt(v, 'Shanghai: ')\n",
    "\n",
    "v = beijing25\n",
    "ReportInt(v, 'Beijing: ')\n",
    "\n",
    "v = guangzhou25\n",
    "ReportInt(v, 'Guangzhou: ')\n",
    "\n",
    "\n",
    "print('Mar 4:======')\n",
    "v = wuhan4\n",
    "ReportInt(v, 'Wuhan: ')\n",
    "\n",
    "v = shanghai4\n",
    "ReportInt(v, 'Shanghai: ')\n",
    "\n",
    "v = beijing4\n",
    "ReportInt(v, 'Beijing: ')\n",
    "\n",
    "v = guangzhou4\n",
    "ReportInt(v, 'Guangzhou: ')\n",
    "\n",
    "\n",
    "print('Total inf:=====')\n",
    "v = np.array(city_cum)[:, nodes['武汉市']]*city_properties['武汉市']['pop']\n",
    "ReportInt(v, 'Wuhan Total Inf.: ')\n",
    "ReportInt(v*death_ratio, 'total death: ')\n",
    "\n",
    "v = np.array(city_cum)[:, nodes['黄冈市']]*city_properties['黄冈市']['pop']\n",
    "ReportInt(v, 'Huanggang Total Inf.: ')\n",
    "ReportInt(v*death_ratio, 'total death: ')\n",
    "\n",
    "v = np.array(city_cum)[:, nodes['重庆市']]*city_properties['重庆市']['pop']\n",
    "ReportInt(v, 'Chongqing Total Inf.: ')\n",
    "ReportInt(v*death_ratio, 'total death: ')\n",
    "\n",
    "v = np.array(city_cum)[:, nodes['上海市']]*city_properties['上海市']['pop']\n",
    "ReportInt(v, 'Shanghai Total Inf.: ')\n",
    "ReportInt(v*death_ratio, 'total death: ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### China total infection, confirmation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T04:38:55.519361Z",
     "start_time": "2020-03-16T04:38:55.323305Z"
    }
   },
   "outputs": [],
   "source": [
    "inf = np.median(np.sum(plotdata[:len(nodes), :, :], axis=0), axis=1)\n",
    "inflow = np.quantile(np.sum(plotdata[:len(nodes), :, :], axis=0), 0.025, axis=1)\n",
    "infhigh = np.quantile(np.sum(plotdata[:len(nodes), :, :], axis=0), 1-0.025, axis=1)\n",
    "\n",
    "conf = np.median(np.sum(plotdata[len(nodes):2*len(nodes), :, :], axis=0), axis=1)\n",
    "conflow = np.quantile(np.sum(plotdata[len(nodes):2*len(nodes), :, :], axis=0), 0.025, axis=1)\n",
    "confhigh = np.quantile(np.sum(plotdata[len(nodes):2*len(nodes), :, :], axis=0), 1-0.025, axis=1)\n",
    "\n",
    "allinf = np.median(np.sum(plotdata[:2*len(nodes), :, :], axis=0), axis=1)\n",
    "allinflow = np.quantile(np.sum(plotdata[:2*len(nodes), :, :], axis=0), 0.025, axis=1)\n",
    "allinfhigh = np.quantile(np.sum(plotdata[:2*len(nodes), :, :], axis=0), 1-0.025, axis=1)\n",
    "\n",
    "chinaplotdata = np.array([range(200), inf, inflow, infhigh, conf, \\\n",
    "          conflow, confhigh, allinf, allinflow, allinfhigh]).T\n",
    "chinaplotdata = chinaplotdata.tolist()\n",
    "chinaplotdata = [['day','infected', 'inf. low', 'inf. high', \\\n",
    " 'confirm', 'con. low', 'con. high', \\\n",
    " 'inf+con', 'i.c.low','i.c.high']] + chinaplotdata\n",
    "\n",
    "with open('./data/plot_china_early_slow.csv', 'w') as csvfile:\n",
    "    writer = csv.writer(csvfile)\n",
    "    writer.writerows(chinaplotdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Varying t0\n",
    "\n",
    "Show how death population change if we can act earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T01:09:28.987868Z",
     "start_time": "2020-03-15T01:08:08.925149Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.066666666666668"
     ]
    }
   ],
   "source": [
    "t0s=np.linspace(1,22,10)\n",
    "#t_i = 9.2#10#1/0.0134#6 #serial interval，参考SARS\n",
    "#t_l = 8.3#8.89 #一个病患潜伏期时间，参考SARS、MERS\n",
    "gamma = 0.03#flowing_ratio\n",
    "eta = 1\n",
    "t_m = 35\n",
    "timespan = np.linspace(0, 200, 1000)\n",
    "interval = 200 / 1000\n",
    "ls0 = np.zeros(len(nodes))\n",
    "js0 = np.zeros(len(nodes))\n",
    "ss0 = np.ones(len(nodes))\n",
    "ls0[nodes['武汉市']] = float(initial_latent)/float(city_properties['武汉市']['pop'])#1e-4\n",
    "js0[nodes['武汉市']] = float(first_cases)/float(city_properties['武汉市']['pop'])\n",
    "ss0[nodes['武汉市']] = 1-ls0[nodes['武汉市']]-js0[nodes['武汉市']]\n",
    "results_cum=[]\n",
    "results_death=[]\n",
    "for t_0 in t0s:\n",
    "    flushPrint(t_0)\n",
    "    cum_all=[]\n",
    "    cum_death=[]\n",
    "    for best_para in experiments:\n",
    "        fit_param = sorted([(vvv[1],i) for i,vvv in enumerate(best_para)])\n",
    "        itm = best_para[fit_param[0][1]]\n",
    "        r0 = itm[0][0][0].item()\n",
    "        initial_latent = itm[0][0][1].item()\n",
    "        alpha = itm[0][0][2].item()\n",
    "        t_i = itm[2][0]#10#1/0.0134#6 #serial interval，参考SARS\n",
    "        t_l = itm[2][1]#8.89 #一个病患潜伏期时间，参考SARS、MERS\n",
    "        gamma = itm[2][2]#0.03#flowing_ratio\n",
    "\n",
    "        result = odeint(diff, np.r_[ls0,js0,ss0], timespan, args = (r0, t_i, t_l, alpha, gamma, eta, \n",
    "                                                                 t_m, t_0, pij,True))\n",
    "        all_pop = np.zeros([result.shape[0]])\n",
    "        all_confirmed = np.zeros([result.shape[0]])\n",
    "        all_recovery = np.zeros([result.shape[0]])\n",
    "        for k,city in city_properties.items():\n",
    "            idx = nodes[k]\n",
    "            all_pop += (result[:, idx]+result[:, len(nodes)+idx]) * city['pop']\n",
    "            all_confirmed += (result[:, len(nodes)+idx]) * city['pop']\n",
    "            all_recovery += (1-result[:, len(nodes)+idx]-result[:,idx]-result[:,2*len(nodes)+idx]) * city['pop']\n",
    "        cum_all.append(np.max(all_recovery))\n",
    "        cum_death.append(np.max(all_recovery * death_ratio))\n",
    "        results_cum.append([t_0, np.max(all_recovery)])\n",
    "        results_death.append([t_0, np.max(all_recovery * death_ratio)])\n",
    "    #results_cum.append([t_0, np.mean(cum_all)])\n",
    "    #results_death.append([t_0, np.mean(cum_death)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-15T03:35:37.213815Z",
     "start_time": "2020-03-15T03:35:37.182775Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slopes: [0.213, 0.212] (0.208-0.211-0.214-0.219)\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "fitdata=np.array(results_death).reshape(10, -1, 2).transpose(1,0,2)\n",
    "slopes = []\n",
    "for i in range(10):\n",
    "    slope = stats.linregress(fitdata[i,:,0],np.log2(fitdata[i,:,1]))[0]\n",
    "    slopes.append(slope)\n",
    "Report(slopes, 'slopes: ', 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relaxation\n",
    "\n",
    "Finding the safe relaxation date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T03:53:55.904054Z",
     "start_time": "2020-03-17T03:44:45.162780Z"
    }
   },
   "outputs": [],
   "source": [
    "t_0 = 22\n",
    "#t_i = 9.2#10#1/0.0134#6 #serial interval，参考SARS\n",
    "#t_l = 8.3#8.89 #一个病患潜伏期时间，参考SARS、MERS\n",
    "#gamma = 0.03#flowing_ratio\n",
    "eta = 1\n",
    "t_m = 35\n",
    "#relax = 88\n",
    "timespan = np.linspace(0, 400, 1000)\n",
    "interval = 400 / 1000\n",
    "ls0 = np.zeros(len(nodes))\n",
    "js0 = np.zeros(len(nodes))\n",
    "ss0 = np.ones(len(nodes))\n",
    "\n",
    "relaxx = []\n",
    "relax_confirmed = []\n",
    "for relax in range(80, 110):#(60, 90):#60-90\n",
    "    for best_para in experiments:\n",
    "        fit_param = sorted([(vvv[1],i) for i,vvv in enumerate(best_para)])\n",
    "        itm = best_para[fit_param[0][1]]\n",
    "        r0 = itm[0][0][0].item()\n",
    "        initial_latent = itm[0][0][1].item()\n",
    "        alpha = itm[0][0][2].item()\n",
    "        t_i = itm[2][0]#10#1/0.0134#6 #serial interval，参考SARS\n",
    "        t_l = itm[2][1]#8.89 #一个病患潜伏期时间，参考SARS、MERS\n",
    "        gamma = itm[2][2]#0.03#flowing_ratio\n",
    "        t_m, t_0, interQ = scenarios['early_slow']\n",
    "        \n",
    "        ls0[nodes['武汉市']] = float(initial_latent)/float(city_properties['武汉市']['pop'])#1e-4\n",
    "        js0[nodes['武汉市']] = float(first_cases)/float(city_properties['武汉市']['pop'])\n",
    "        ss0[nodes['武汉市']] = 1-ls0[nodes['武汉市']]-js0[nodes['武汉市']]\n",
    "        cum_inf_conf = np.zeros(len(nodes))\n",
    "\n",
    "        result = odeint(diff, np.r_[ls0,js0,ss0], timespan, args = (r0, t_i, t_l, alpha, gamma, eta, \n",
    "                                                                 t_m, t_0, pij,True,relax))\n",
    "        number = 0\n",
    "        all_pop = np.zeros([result.shape[0]])\n",
    "        all_confirmed = np.zeros([result.shape[0]])\n",
    "        all_recovery = np.zeros([result.shape[0]])            \n",
    "        for city,c in nodes.items():\n",
    "            rr = result[:,nodes[city]]*city_properties[city]['pop']\n",
    "            all_pop += (result[:, c]+result[:, len(nodes)+c]) * city_properties[city]['pop']\n",
    "            all_confirmed += (result[:, len(nodes)+c]) * city_properties[city]['pop']\n",
    "            #print(2*len(nodes)+c)\n",
    "            #print('idx',idx)\n",
    "            all_recovery += (1-result[:, len(nodes)+c]-result[:,c]-\n",
    "                             result[:,2*len(nodes)+c]) * city_properties[city]['pop']\n",
    "            bb = np.nonzero((rr<1)&(timespan>relax)&(timespan<250))\n",
    "            if len(bb[0])>0:\n",
    "                number += 1 #人数小于1的城市数\n",
    "        if number == len(nodes): #若所有城市都小于1，则存储当前日期以及确诊人数\n",
    "            relaxx.append(relax)\n",
    "            c = all_confirmed[int(relax / interval)]\n",
    "            relax_confirmed.append(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T04:33:07.740034Z",
     "start_time": "2020-03-17T04:33:07.735553Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(relaxx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T04:33:09.187596Z",
     "start_time": "2020-03-17T04:33:09.111250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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87GhddgB3j6e68ZtvfE67Rv52BpecznULHTP7gevap5OuBE6cupxyDpp3jJL8fJK0+SsY/E7777FUuDbtB65N8vwklzH4IMO/LblVVfka8gV8Afg6g8skW1vbRQw+NXG4TS8cd51rbHz+Hvga8GA7aC8dd52rPCZ3MriE9iMG/5vbudAxw+AywIeB/2hjNjXu+tfgGL0beLgdY/cBrx13/WMan7e3+R8ATwCfntP/j9sx9Aiw7Wz24ZPPkqSOl5IkSR2DQZLUMRgkSR2DQZLUMRgkSR2DQZLUMRgkSR2DQZLU+X9zA7yxMxDcMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(relaxx)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-17T04:33:14.019821Z",
     "start_time": "2020-03-17T04:33:14.012501Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94.0\n",
      "relaxation: [9119, 9105] (5804-8016-10318-12006)\n"
     ]
    }
   ],
   "source": [
    "#bools = np.array(relaxx) == min(relaxx)\n",
    "bools = np.array(relaxx) == np.quantile(relaxx, 0.05)\n",
    "relax_result = np.array(relax_confirmed)[bools]\n",
    "\n",
    "print(np.quantile(relaxx, 0.05))\n",
    "\n",
    "v=relax_result\n",
    "ReportInt(v, 'relaxation: ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-13T05:57:07.364027Z",
     "start_time": "2020-03-13T05:57:06.314371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_para=experiments[1]\n",
    "fit_param = sorted([(vvv[1],i) for i,vvv in enumerate(best_para)])\n",
    "itm = best_para[fit_param[0][1]]\n",
    "r0 = itm[0][0][0].item()\n",
    "initial_latent = itm[0][0][1].item()\n",
    "alpha = itm[0][0][2].item()\n",
    "\n",
    "result = odeint(diff, np.r_[ls0,js0,ss0], timespan, args = (r0, t_i, t_l, alpha, gamma, eta, \n",
    "                                                         t_m, t_0, pij,True,90)) #81\n",
    "number = 0\n",
    "rall = np.zeros(1000)\n",
    "for city,c in nodes.items():\n",
    "    rr = result[:,nodes[city]]*city_properties[city]['pop']\n",
    "    rall += rr\n",
    "    dd = np.abs(np.diff(rr))\n",
    "    bb = np.nonzero((rr<1)&(timespan>relax)&(timespan<400))\n",
    "    if len(bb[0])>0:\n",
    "        number += 1\n",
    "    plt.semilogy(timespan, rr)\n",
    "plt.plot(timespan, np.ones(len(timespan)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-13T06:52:09.039072Z",
     "start_time": "2020-03-13T06:52:03.647758Z"
    }
   },
   "outputs": [],
   "source": [
    "conf_alls = []\n",
    "timespan = np.linspace(0, 400, 1000)\n",
    "interval = 400 / 1000\n",
    "for best_para in experiments:\n",
    "    #best_para=experiments[1]\n",
    "    fit_param = sorted([(vvv[1],i) for i,vvv in enumerate(best_para)])\n",
    "    itm = best_para[fit_param[0][1]]\n",
    "    r0 = itm[0][0][0].item()\n",
    "    initial_latent = itm[0][0][1].item()\n",
    "    alpha = itm[0][0][2].item()\n",
    "    t_m, t_0, interQ = scenarios['baseline']\n",
    "    \n",
    "    ls0 = np.zeros(len(nodes))\n",
    "    js0 = np.zeros(len(nodes))\n",
    "    ss0 = np.ones(len(nodes))\n",
    "    ls0[nodes['武汉市']] = float(initial_latent)/float(city_properties['武汉市']['pop'])#1e-4\n",
    "    js0[nodes['武汉市']] = float(first_cases)/float(city_properties['武汉市']['pop'])\n",
    "    ss0[nodes['武汉市']] = 1-ls0[nodes['武汉市']]-js0[nodes['武汉市']]\n",
    "\n",
    "    result = odeint(diff, np.r_[ls0,js0,ss0], timespan, args = (r0, t_i, t_l, alpha, gamma, eta, \n",
    "                                                             t_m, t_0, pij,True,95)) #81\n",
    "    number = 0\n",
    "    rall = np.zeros(1000)\n",
    "    for city,c in nodes.items():\n",
    "        rr = result[:,nodes[city]]*city_properties[city]['pop']\n",
    "        rall += rr\n",
    "        dd = np.abs(np.diff(rr))\n",
    "        bb = np.nonzero((rr<1)&(timespan>relax)&(timespan<400))\n",
    "        if len(bb[0])>0:\n",
    "            number += 1\n",
    "    conf_alls.append(rall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-13T06:52:09.200869Z",
     "start_time": "2020-03-13T06:52:09.040302Z"
    }
   },
   "outputs": [],
   "source": [
    "median90=[np.median(confs) for confs in np.array(conf_alls).T]\n",
    "low90=[np.quantile(confs,0.025) for confs in np.array(conf_alls).T]\n",
    "high90=[np.quantile(confs,1-0.025) for confs in np.array(conf_alls).T]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-13T06:52:09.332456Z",
     "start_time": "2020-03-13T06:52:09.201883Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(timespan, median90)\n",
    "plt.fill_between(timespan, low90, high90, alpha=0.2, facecolor='#000000')\n",
    "#plt.plot(timespan, low90)\n",
    "#plt.plot(timespan, high90)\n",
    "plt.plot(timespan, np.ones(len(timespan)))\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-13T06:52:11.513218Z",
     "start_time": "2020-03-13T06:52:11.505451Z"
    }
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "with open('90days.csv','w') as csvfile:\n",
    "    writer = csv.writer(csvfile)\n",
    "    writer.writerows(np.array([median90,low90,high90]).T)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "262.9891357421875px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
